Biology subjects

Gresham, D. J.

Publications and source records attributed to Gresham, D. J..

6 recordsLinked to original sources

bioRxiv2025

Intracellular replication dynamics of influenza A virus impose strong bottleneck effects

Understanding the sources of genetic diversity in Influenza A Virus (IAV) infections is crucial for understanding the mechanisms of viral evolution and immune escape. Whereas prior studies have characterized the effects of population bottlenecks during host-to-host transmission and intrahost tissue-to-tissue dissemination, the role of intracellular replication processes on IAV genetic diversity remains largely unexplored. In this study, we used stochastic mathematical modeling to simulate the replication of genetically distinct IAV strains within individual cells and tissues. Our results reveal significant bottleneck effects within a single infection cycle of individual cells. Intracellular bottleneck effects are driven by stochastic molecular processes and lead to the expansion or elimination of neutral variants creating large-scale differences between the initial and final frequencies of genetic variants in individual cells. By expanding our findings to a population-level tissue model, we show that IAV intracellular replication reduces the effective population size, thereby diminishing the impact of selection and increasing the role of genetic drift. Our findings highlight the important contribution of intracellular replication processes to the generation of genetic diversity in IAV.

evolutionary biology↗

bioRxiv2024

Patient Subtyping Analysis of Baseline Multi-omic Data Reveals Distinct Pre-immune States Predictive of Vaccination Responses

Understanding the molecular mechanisms that underpin diverse vaccination responses is a critical step toward developing efficient vaccines. Molecular subtyping approaches can offer valuable insights into the heterogeneous nature of responses and aid in the design of more effective vaccines. In order to explore the molecular signatures associated with the vaccine response, we analyzed baseline transcriptomics data from paired samples of whole blood, proteomics and glycomics data from serum, and metabolomics data from urine, obtained from influenza vaccine recipients (2019-2020 season) prior to vaccination. After integrating the data using a network-based model, we performed a subtyping analysis. The integration of multiple data modalities from 62 samples resulted in five baseline molecular subtypes with distinct molecular signatures. These baseline subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation across subtypes in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these significant differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining vaccine response and efficacy. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.

immunology↗

bioRxiv2023

Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression

Cells respond to environmental and developmental stimuli by remodeling their transcriptomes through regulation of both mRNA transcription and mRNA decay. A central goal of biology is identifying the global set of regulatory relationships between factors that control mRNA production and degradation and their target transcripts and construct a predictive model of gene expression. Regulatory relationships are typically identified using transcriptome measurements and causal inference algorithms. RNA kinetic parameters are determined experimentally by employing run-on or metabolic labeling (e.g. 4-thiouracil) methods that allow transcription and decay rates to be separately measured. Here, we develop a deep learning model, trained with single-cell RNA-seq data, that both infers causal regulatory relationships and estimates RNA kinetic parameters. The resulting in silico model predicts future gene expression states and can be perturbed to simulate the effect of transcription factor changes. We acquired model training data by sequencing the transcriptomes of 175,000 individual Saccharomyces cerevisiae cells that were subject to an external perturbation and continuously sampled over a one hour period. The rate of change for each transcript was calculated on a per-cell basis to estimate RNA velocity. We then trained a deep learning model with transcriptome and RNA velocity data to calculate time-dependent estimates of mRNA production and decay rates. By separating RNA velocity into transcription and decay rates, we show that rapamycin treatment causes existing ribosomal protein transcripts to be rapidly destabilized, while production of new transcripts gradually slows over the course of an hour. The neural network framework we present is designed to explicitly model causal regulatory relationships between transcription factors and their genes, and shows superior performance to existing models on the basis of recovery of known regulatory relationships. We validated the predictive power of the model by perturbing transcription factors in silico and comparing transcriptome-wide effects with experimental data. Our study represents the first step in constructing a complete, predictive, biophysical model of gene expression regulation.

systems biology↗

bioRxiv2023

Bacterial and molecular contamination of a small freshwater effluent used for drinking water

Sewage contamination of freshwater occurs in the form of raw waste or as effluent (at varying levels of treatment) from wastewater treatment plants. Global management of this contamination has focused on detection of live sewage-indicating bacteria in freshwater, drinking water, and irrigation systems. While raw waste (animal and human) and underfunctioning WWTPs can introduce live enteric bacteria to freshwater systems, most WWTPs, even when operating correctly, do not remove bacterial genetic material from treated waste, resulting in the addition of concentrated enteric bacterial DNA (molecular contamination), including antibiotic resistance genes, into water columns and sediment of freshwater systems. In freshwater systems with both raw and treated waste inputs, then, there will be increased interaction between live sewage-associated bacteria (untreated sewage) and molecular contamination (from both untreated and treated wastewater effluent), with the potential of increasing antibiotic resistance in the live bacterial populations. To evaluate this understudied interaction between molecular and bacterial contamination in the freshwater environment, we conducted a three-month field-based study of sewage-associated bacteria and genetic material in water and sediment in a freshwater tributary of the Hudson River (NY, USA) that supplies drinking water and receives treated and untreated wastewater discharges from several municipalities. Using both molecular and culture-based bacterial analyses, we demonstrate both treated and untreated sewage influences on water and sediment bacterial communities in this tributary, and water-sediment exchanges of enteric bacteria and associated genetic material with rain events. Furthermore, treated sewage effluent on this waterway serves as a concentrated source of int1 (antibiotic resistance) genes, which appear to collect in the sediments below the outfall along with fecal indicating bacteria, serving as a possible genetic exchange substrate and a source for future molecular and bacterial water contamination. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/530486v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1abdb61org.highwire.dtl.DTLVardef@1cfc550org.highwire.dtl.DTLVardef@1a33647org.highwire.dtl.DTLVardef@40829e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIIn a model freshwater system used both as drinking water and wastewater disposal, C_LIO_LIBacterial and genetic material differ between water and sediment compartments C_LIO_LILive bacteria C_LI

microbiology↗

bioRxiv2023

Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

The modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of regulatory features in genome-wide screens. Most GRN inference methods are therefore forced to model relationships between regulatory genes and their targets with expression as a proxy for the upstream independent features, complicating validation and predictions produced by modeling frameworks. Separating covariance and regulatory influence requires aggregation of independent and complementary sets of evidence, such as transcription factor (TF) binding and target gene expression. However, the complete regulatory state of the system, e.g. TF activity (TFA) is unknown due to a lack of experimental feasibility, making regulatory relations difficult to infer. Some methods attempt to account for this by modeling TFA as a latent feature, but these models often use linear frameworks that are unable to account for non-linearities such as saturation, TF-TF interactions, and other higher order features. Deep learning frameworks may offer a solution, as they are capable of modeling complex interactions and capturing higher-order latent features. However, these methods often discard central concepts in biological systems modeling, such as sparsity and latent feature interpretability, in favor of increased model complexity. We propose a novel deep learning autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor), that scales to single cell genomic data and maintains interpretability to perform GRN inference and estimate TFA as a latent feature. We demonstrate that SupirFactor outperforms current leading GRN inference methods, predicts biologically relevant TFA and elucidates functional regulatory pathways through aggregation of TFs.

systems biology↗

bioRxiv2019

Regulatory kinase genetic interaction profiles differ between environmental conditions and cellular states

Cell growth and quiescence in eukaryotic cells is controlled by an evolutionarily conserved network of signaling pathways. Signal transduction networks operate to modulate a wide range of cellular processes and physiological properties when cells exit proliferative growth and initiate a quiescent state. How signaling networks function to respond to diverse signals that result in cell cycle exit and establishment of a quiescent state is poorly understood. Here, we studied the function of signaling pathways in quiescent cells using global genetic interaction mapping in the model eukaryotic cell, Saccharomyces cerevisiae (budding yeast). We performed pooled analysis of genotypes using molecular barcode sequencing to test the role of [~]3,900 gene deletion mutants and [~]11,700 pairwise interactions between all non-essential genes and the protein kinases TOR1, RIM15, PHO85 in three different nutrient-restricted conditions in both proliferative and quiescent cells. We detect nearly five-fold more genetic interactions in quiescent cells compared to proliferative cells. We find that both individual gene effects and genetic interaction profiles vary depending on the specific pro-quiescence signal. The master regulator of quiescence, RIM15 shows distinct genetic interaction profiles in response to different starvation signals. However, vacuole-related functions show consistent genetic interactions with RIM15 in response to different starvation signals suggesting that RIM15 integrates diverse signals to maintain protein homeostasis in quiescent cells. Our study expands genome-wide genetic interaction profiling to additional conditions, and phenotypes, highlighting the conditional dependence of epistasis.

genetics↗